scholarly journals Non Invasive Live Cell Cycle Monitoring using Quantitative Phase Imaging and Proximal Machine Learning Methods

2019 ◽  
Author(s):  
Pognonec Philippe ◽  
Barlaud Michel ◽  
Wattellier Benoit ◽  
Pourcher Thierry ◽  
Zhou Yuxiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Cycle ◽  
Live Cell ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Learning Methods ◽  
Quantitative Phase ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
Cycle Monitoring

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

2020 ◽  
Author(s):  
L. Sheneman ◽  
G. Stephanopoulos ◽  
A. E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Learning Methods ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

AbstractWe report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all machine learning methods that we implemented, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism of single cells.Author SummaryRecently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Bote ◽  
J F Ortega-Morán ◽  
C L Saratxaga ◽  
B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Structural Information ◽  
Ex Vivo ◽  
Ground Truth ◽  
Colon Polyps ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

scholarly journals Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

2020 ◽  
Vol 25 (02) ◽  
pp. 1 ◽  
Author(s):  
Van K. Lam ◽  
Thanh C. Nguyen ◽  
Vy Bui ◽  
Byung Min Chung ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Quantitative Scoring

Predicting advanced fibrosis using non-invasive clinical tests and modern machine learning methods in TARGET-NASH

2020 ◽  
Vol 73 ◽  
pp. S431-S432
Author(s):  
Peter Mesenbrink ◽  
A. Sidney Barritt ◽  
Rohit Loomba ◽  
Philip N Newsome ◽  
Arun Sanyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Advanced Fibrosis ◽  
Clinical Tests ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
Modern Machine

scholarly journals Dry Mass and Cell Cycle Follow-Up from Quantitative Phase Imaging

2014 ◽  
Vol 106 (2) ◽  
pp. 575a
Author(s):  
Julien Savatier ◽  
Sherazade Aknoun ◽  
Pierre Bon ◽  
Lamiae Abdeladim ◽  
Benoit Wattellier ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Dry Mass ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase
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